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476455e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 | # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
from __future__ import absolute_import
import numpy as np
import os
import json
from sagemaker.pytorch.model import PyTorchModel
from sagemaker.utils import sagemaker_timestamp
from sagemaker.predictor import Predictor
from tests.integ import (
DATA_DIR,
)
from tests.integ.timeout import timeout_and_delete_endpoint_by_name
NEO_DIR = os.path.join(DATA_DIR, "pytorch_neo")
NEO_MODEL = os.path.join(NEO_DIR, "model.tar.gz")
NEO_INFERENCE_IMAGE = os.path.join(NEO_DIR, "cat.jpg")
NEO_IMAGENET_CLASSES = os.path.join(NEO_DIR, "imagenet1000_clsidx_to_labels.txt")
NEO_CODE_DIR = os.path.join(NEO_DIR, "code")
NEO_SCRIPT = os.path.join(NEO_CODE_DIR, "inference.py")
def test_compile_and_deploy_model_with_neo(
sagemaker_session,
neo_pytorch_cpu_instance_type,
neo_pytorch_latest_version,
neo_pytorch_latest_py_version,
neo_pytorch_target_device,
neo_pytorch_compilation_job_name,
):
endpoint_name = "test-neo-pytorch-deploy-model-{}".format(sagemaker_timestamp())
model_data = sagemaker_session.upload_data(path=NEO_MODEL)
bucket = sagemaker_session.default_bucket()
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
model = PyTorchModel(
model_data=model_data,
predictor_cls=Predictor,
role="SageMakerRole",
entry_point=NEO_SCRIPT,
source_dir=NEO_CODE_DIR,
framework_version=neo_pytorch_latest_version,
py_version=neo_pytorch_latest_py_version,
sagemaker_session=sagemaker_session,
env={"MMS_DEFAULT_RESPONSE_TIMEOUT": "500"},
)
data_shape = '{"input0":[1,3,224,224]}'
compiled_model_path = "s3://{}/{}/output".format(bucket, neo_pytorch_compilation_job_name)
compiled_model = model.compile(
target_instance_family=neo_pytorch_target_device,
input_shape=data_shape,
job_name=neo_pytorch_compilation_job_name,
role="SageMakerRole",
framework="pytorch",
framework_version=neo_pytorch_latest_version,
output_path=compiled_model_path,
)
# Load names for ImageNet classes
object_categories = {}
with open(NEO_IMAGENET_CLASSES, "r") as f:
for line in f:
if line.strip():
key, val = line.strip().split(":")
object_categories[key] = val
with open(NEO_INFERENCE_IMAGE, "rb") as f:
payload = f.read()
payload = bytearray(payload)
predictor = compiled_model.deploy(
1, neo_pytorch_cpu_instance_type, endpoint_name=endpoint_name
)
response = predictor.predict(payload)
result = json.loads(response.decode())
assert "tiger cat" in object_categories[str(np.argmax(result))]
assert compiled_model.framework_version == neo_pytorch_latest_version
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